bug fix: DLModel prediction (intel-analytics#2194)

* bug fix: DLModel prediction (#4)

Make sure DLModel.train=False when predicting in pipeline API

* 1. broadcast transformer in DLModel.transform ; 2. remove useless ut

spark/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala

@@ -387,11 +387,13 @@ class DLModel[@specialized(Float, Double) T: ClassTag](
     val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol))
     val featureFunc = getConvertFunc(featureType)
     val sc = dataFrame.sqlContext.sparkContext
-    val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model)
+    val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model.evaluate())
     val localBatchSize = $(batchSize)
+    val transformerBC = sc.broadcast(SampleToMiniBatch[T](localBatchSize))
 
     val resultRDD = dataFrame.rdd.mapPartitions { rowIter =>
       val localModel = modelBroadCast.value()
+      val transformer = transformerBC.value.cloneTransformer()
       rowIter.grouped(localBatchSize).flatMap { rowBatch =>
         val samples = rowBatch.map { row =>
           val features = featureFunc(row, featureColIndex)
@@ -401,7 +403,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag](
           }
           Sample(Tensor(featureBuffer.toArray, featureSize))
         }.toIterator
-        val predictions = SampleToMiniBatch(localBatchSize).apply(samples).flatMap { batch =>
+        val predictions = transformer(samples).flatMap { batch =>
           val batchResult = localModel.forward(batch.getInput())
           batchResult.toTensor.split(1).map(outputToPrediction)
         }